In silico search for new selective Nav1.2 blockers for the treatment of Dravet syndrome

FALLICO, MAXIMILIANO; PRADA GORI, DENIS N.; LLANOS MANUEL; ALBERCA LUCAS NICOLAS; GAVERNET LUCIANA; TALEVI ALAN

Conferencia; Reunion conjunta de sociedades de biociencias; 2022

Dravet syndrome is severe childhood epilepsy, characterized by loss-of-function mutations in NaV1.1 sodium channels. Here, we pursued the computer-aided identification of compounds that selectively block Nav1.2, without blocking Nav1.1, as potential treatments. For that purpose, we resorted to a combination of ligand-based and structure-based models. Two datasets of compounds previously assayed against Nav1.1 and Nav1.2 were compiled from the literature. After data curation, 91 and 167 compounds tested against Nav1.1 and Nav1.2 were kept. 2000 and 4000 linear classifiers based on random subspace exploration of Mordred molecular descriptors were built for Nav1.1 and Nav1.2, respectively. Model ensembles were selectively obtained using the area under the ROC curve in retrospective screens as a selection parameter. The top 12 and 11 models for Nav1.1 and Nav1.2 were combined, in that order, using the MIN operator. Two repurposing databases, DrugBank and Drug Repurposing Hub, were then prospectively screened. Compounds selected as Nav1.2 blockers were subsequently screened by the ensemble of Nav1.1 models, which is used here as an anti-target, to ensure selectivity. 40 selective in silico hits were selected and further analyzed using molecular docking. The candidates were docked into the experimental 3D structure of Nav1.2, and the binding interactions were analyzed. 3 of the resulting hits were acquired and submitted for experimental confirmation in patch clamp. Our results confirmed the predictivity of our hybrid approach to detect new anticonvulsant agents with potential applications as Dravet treatment.